An educational resource to help anyone learn deep reinforcement learning.

Last update: Jan 09, 2023

Related tags

Overview

Status: Maintenance (expect bug fixes and minor updates)

Welcome to Spinning Up in Deep RL!

This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL).

For the unfamiliar: reinforcement learning (RL) is a machine learning approach for teaching agents how to solve tasks by trial and error. Deep RL refers to the combination of RL with deep learning.

This module contains a variety of helpful resources, including:

a short introduction to RL terminology, kinds of algorithms, and basic theory,
an essay about how to grow into an RL research role,
a curated list of important papers organized by topic,
a well-documented code repo of short, standalone implementations of key algorithms,
and a few exercises to serve as warm-ups.

Get started at spinningup.openai.com!

Citing Spinning Up

If you reference or use Spinning Up in your research, please cite:

@article{SpinningUp2018,
    author = {Achiam, Joshua},
    title = {{Spinning Up in Deep Reinforcement Learning}},
    year = {2018}
}

Comments

Running spinningup in Linux Subsystem on Windows (Success)
For people who are on windows 10 and do not have linux but want to make things work.

you can enable the WSL in your windows 10 following this.

Install Xming X window server for windows from here. and make sure it is running.

Once WSL is working : Open cmd, type in "bash", this will switch the cmd to WSL terminal, then run the following it will enable GUI for WSL . Copied from this stackoverflow answer.

sudo apt-get install x11-apps export DISPLAY=localhost:0.0 nano ~/.bashrc #(add export DISPLAY=localhost:0.0 at the end. Ctrl+X to exit/save) sudo apt-get install gnome-calculator #will get you GTK

Download miniconda for linux from here. It will be an ".sh" file.

from the terminal go to the folder you downloaded the file to and run "bash <name_of_downloaded_file>", this will install conda.

follow the spinningup tutorial for rest of installation.
opened by ibrahiminfinite 33
OMP: Error #15: Initializing libiomp5.dylib
Followed Mujoco install, followed by full Gym installation.

Used Miniconda, py 3.6

On fresh OSX Mojave (right out of the box!)

Ran the following code:

import gym import tensorflow as tf from spinup import ddpg env_name = 'Pendulum-v0' env_fn = lambda : gym.make(env_name) ac_kwargs = { 'hidden_sizes':[64,64], 'activation' :tf.nn.relu } logger_kwargs = { 'output_dir' : 'logs', 'exp_name' :'pendulum_test' } addl_kwargs = { 'seed' : 42 } ddpg(env_fn, ac_kwargs=ac_kwargs, logger_kwargs=logger_kwargs, **addl_kwargs)

Received following error on first build:

INFO:tensorflow:Assets added to graph. INFO:tensorflow:No assets to write. INFO:tensorflow:SavedModel written to: logs/simple_save/saved_model.pb

OMP: Error #15: Initializing libiomp5.dylib, but found libiomp5.dylib already initialized.

OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into theprogram. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.

[MBP:05587] *** Process received signal *** [MBP:05587] Signal: Abort trap: 6 (6) [MBP:05587] Signal code: (0) [MBP:05587] [ 0] 0 libsystem_platform.dylib 0x00007fff6ad79b3d _sigtramp + 29 [MBP:05587] [ 1] 0 libiomp5.dylib 0x0000000110b7b018 __kmp_openmp_version + 88572 [MBP:05587] [ 2] 0 libsystem_c.dylib 0x00007fff6ac381c9 abort + 127 [MBP:05587] [ 3] 0 libiomp5.dylib 0x0000000110b24df3 __kmp_abort_process + 35 [MBP:05587] *** End of error message *** Abort trap: 6

The workaround is including:

import os os.environ['KMP_DUPLICATE_LIB_OK']='True'

However, is there a more permanent fix? Why might there be multiple instances of OpenMP?
opened by ZachariahRosenberg 19
Installation issue
I get a weird error when i test my installation using

python -m spinup.run ppo --hid [32,32] --env Walker2d-v2 --exp_name installtest

Error :

`================================================================================ ExperimentGrid [installtest] runs over parameters:

env_name [env]

Walker2d-v2

ac_kwargs:hidden_sizes [ac-hid]

[32, 32]

Variants, counting seeds: 1 Variants, not counting seeds: 1

================================================================================

Preparing to run the following experiments...

installtest

================================================================================

Launch delayed to give you a few seconds to review your experiments.

To customize or disable this behavior, change WAIT_BEFORE_LAUNCH in spinup/user_config.py.

================================================================================ Running experiment:

installtest

with kwargs:

{ "ac_kwargs": { "hidden_sizes": [ 32, 32 ] }, "env_name": "Walker2d-v2", "seed": 0 }

Traceback (most recent call last): File "/Users/haresh/Documents/spinningup/spinup/utils/run_entrypoint.py", line 10, in thunk = pickle.loads(zlib.decompress(base64.b64decode(args.encoded_thunk))) File "/usr/local/Cellar/[email protected]/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 1388, in loads return Unpickler(file).load() File "/usr/local/Cellar/[email protected]/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 864, in load dispatchkey File "/usr/local/Cellar/[email protected]/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 892, in load_proto raise ValueError, "unsupported pickle protocol: %d" % proto ValueError: unsupported pickle protocol: 4

================================================================================

There appears to have been an error in your experiment.

Check the traceback above to see what actually went wrong. The traceback below, included for completeness (but probably not useful for diagnosing the error), shows the stack leading up to the experiment launch.

================================================================================

Traceback (most recent call last): File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/runpy.py", line 193, in _run_module_as_main "main", mod_spec) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/runpy.py", line 85, in _run_code exec(code, run_globals) File "/Users/haresh/Documents/spinningup/spinup/run.py", line 230, in parse_and_execute_grid_search(cmd, args) File "/Users/haresh/Documents/spinningup/spinup/run.py", line 162, in parse_and_execute_grid_search eg.run(algo, **run_kwargs) File "/Users/haresh/Documents/spinningup/spinup/utils/run_utils.py", line 546, in run data_dir=data_dir, datestamp=datestamp, **var) File "/Users/haresh/Documents/spinningup/spinup/utils/run_utils.py", line 171, in call_experiment subprocess.check_call(cmd, env=os.environ) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/subprocess.py", line 291, in check_call raise CalledProcessError(retcode, cmd) subprocess.CalledProcessError: Command '['python', '/Users/haresh/Documents/spinningup/spinup/utils/run_entrypoint.py', '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']' returned non-zero exit status 1.`
opened by HareshKarnan 13
Plotting error after first installation

As a heads up some users might run into the following error when attempting to plot the performance of their agent when checking their installation the first time:

ImportError: Python is not installed as a framework. The Mac OS X backend will not be able to function correctly if Python is not installed as a framework. See the Python documentation for more information on installing Python as a framework on Mac OS X. Please either reinstall Python as a framework, or try one of the other backends. If you are using (Ana)Conda please install python.app and replace the use of 'python' with 'pythonw'. See 'Working with Matplotlib on OSX' in the Matplotlib FAQ for more information.

In order to overcome this, they can follow the steps outlined here

I have no clue if this only happens in a virtual environment (I was personally using a Conda environment) and didn't know where else to put it. Hope this helps!

opened by cyrilzakka 10
Eager Execution Support

Having the code base in some dynamic graph style library would be great. Pytorch does this well, but since google has released eager execution, having the codebase in eager execution mode will be really nice to have since the codebase is a learning tool. I was having a bit of trouble understanding the session based control of tensorflow (coming from a pytorch background) for the vanilla policy gradient, inspite of it being written really well :')

I would be happy to slowly convert some of the codebase to support eager execution. Do you think this would be useful ?

opened by karanchahal 9
Cannot run the first example on Mac OS.

I am installing Spinning Up on Mac OS. I followed this link After this, to see if you’ve successfully installed Spinning Up, I tried running PPO in the LunarLander-v2 environment with python -m spinup.run ppo --hid "[32,32]" --env LunarLander-v2 --exp_name installtest --gamma 0.999 Then an error pops up

I have no idea about this error. Can anyone give me help?

opened by zhjmcjk 9

Valid Gym environments to use!

Hi,

I've tried spinningup in running many experiments using the different algorithms in different Gym environments. It works well in most environments, like Atari, Box2D, Classic control and MuJoCo, however it didn't work with the new gym environments of "Robotics".

For example when I run the following command on terminal: python -m spinup.run ppo --env FetchReach-v1 --exp_name FetchReach

It shows:

================================================================================
ExperimentGrid [FetchReachExp] runs over parameters:

 env_name                                 [env] 

	FetchReach-v1

 Variants, counting seeds:               1
 Variants, not counting seeds:           1

================================================================================

Preparing to run the following experiments...

FetchReachExp

================================================================================

Launch delayed to give you a few seconds to review your experiments.

To customize or disable this behavior, change WAIT_BEFORE_LAUNCH in
spinup/user_config.py.

================================================================================
Launching in...:                                                       Launching in...: ██▌                                               | 00Launching in...: █████                                             | 00Launching in...: ███████▌                                          | 00Launching in...: ██████████                                        | 00Launching in...: ████████████▌                                     | 00Launching in...: ███████████████                                   | 00Launching in...: █████████████████▌                                | 00Launching in...: ████████████████████                              | 00Launching in...: ██████████████████████▌                           | 00Launching in...: █████████████████████████                         | 00Launching in...: ███████████████████████████▌                      | 00Launching in...: ██████████████████████████████                    | 00Launching in...: ████████████████████████████████▌                 | 00Launching in...: ███████████████████████████████████               | 00Launching in...: █████████████████████████████████████▌            | 00Launching in...: ████████████████████████████████████████          | 00Launching in...: ██████████████████████████████████████████▌       | 00Launching in...: █████████████████████████████████████████████     | 00Launching in...: ███████████████████████████████████████████████▌  | 00Launching in...: ██████████████████████████████████████████████████| 00                                                                       Running experiment:

FetchReachExp

with kwargs:

{
    "env_name":	"FetchReach-v1",
    "seed":	0
}


Logging data to /home/sketcher/MachineLearning/DRL/OpenAI/spinningup/data/FetchReachExp/FetchReachExp_s0/progress.txt
Saving config:

{
    "ac_kwargs":	{},
    "actor_critic":	"mlp_actor_critic",
    "clip_ratio":	0.2,
    "env_fn":	"<function call_experiment.<locals>.thunk_plus.<locals>.<lambda> at 0x7f245efad488>",
    "epochs":	100,
    "exp_name":	"FetchReachExp",
    "gamma":	0.99,
    "lam":	0.97,
    "logger":	{
        "<spinup.utils.logx.EpochLogger object at 0x7f245efbd9b0>":	{
            "epoch_dict":	{},
            "exp_name":	"FetchReachExp",
            "first_row":	true,
            "log_current_row":	{},
            "log_headers":	[],
            "output_dir":	"/home/sketcher/MachineLearning/DRL/OpenAI/spinningup/data/FetchReachExp/FetchReachExp_s0",
            "output_file":	{
                "<_io.TextIOWrapper name='/home/sketcher/MachineLearning/DRL/OpenAI/spinningup/data/FetchReachExp/FetchReachExp_s0/progress.txt' mode='w' encoding='UTF-8'>":	{
                    "mode":	"w"
                }
            }
        }
    },
    "logger_kwargs":	{
        "exp_name":	"FetchReachExp",
        "output_dir":	"/home/sketcher/MachineLearning/DRL/OpenAI/spinningup/data/FetchReachExp/FetchReachExp_s0"
    },
    "max_ep_len":	1000,
    "pi_lr":	0.0003,
    "save_freq":	10,
    "seed":	0,
    "steps_per_epoch":	4000,
    "target_kl":	0.01,
    "train_pi_iters":	80,
    "train_v_iters":	80,
    "vf_lr":	0.001
}
Traceback (most recent call last):
  File "/home/sketcher/MachineLearning/DRL/OpenAI/spinningup/spinup/utils/run_entrypoint.py", line 11, in <module>
    thunk()
  File "/home/sketcher/MachineLearning/DRL/OpenAI/spinningup/spinup/utils/run_utils.py", line 162, in thunk_plus
    thunk(**kwargs)
  File "/home/sketcher/MachineLearning/DRL/OpenAI/spinningup/spinup/algos/ppo/ppo.py", line 183, in ppo
    x_ph, a_ph = core.placeholders_from_spaces(env.observation_space, env.action_space)
  File "/home/sketcher/MachineLearning/DRL/OpenAI/spinningup/spinup/algos/ppo/core.py", line 27, in placeholders_from_spaces
    return [placeholder_from_space(space) for space in args]
  File "/home/sketcher/MachineLearning/DRL/OpenAI/spinningup/spinup/algos/ppo/core.py", line 27, in <listcomp>
    return [placeholder_from_space(space) for space in args]
  File "/home/sketcher/MachineLearning/DRL/OpenAI/spinningup/spinup/algos/ppo/core.py", line 24, in placeholder_from_space
    raise NotImplementedError
NotImplementedError



================================================================================


There appears to have been an error in your experiment.

Check the traceback above to see what actually went wrong. The 
traceback below, included for completeness (but probably not useful
for diagnosing the error), shows the stack leading up to the 
experiment launch.

================================================================================



Traceback (most recent call last):
  File "/home/sketcher/anaconda3/envs/OpAI-env/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/sketcher/anaconda3/envs/OpAI-env/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/sketcher/MachineLearning/DRL/OpenAI/spinningup/spinup/run.py", line 230, in <module>
    parse_and_execute_grid_search(cmd, args)
  File "/home/sketcher/MachineLearning/DRL/OpenAI/spinningup/spinup/run.py", line 162, in parse_and_execute_grid_search
    eg.run(algo, **run_kwargs)
  File "/home/sketcher/MachineLearning/DRL/OpenAI/spinningup/spinup/utils/run_utils.py", line 546, in run
    data_dir=data_dir, datestamp=datestamp, **var)
  File "/home/sketcher/MachineLearning/DRL/OpenAI/spinningup/spinup/utils/run_utils.py", line 171, in call_experiment
    subprocess.check_call(cmd, env=os.environ)
  File "/home/sketcher/anaconda3/envs/OpAI-env/lib/python3.7/subprocess.py", line 347, in check_call
    raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['/home/sketcher/anaconda3/envs/OpAI-env/bin/python', '/home/sketcher/MachineLearning/DRL/OpenAI/spinningup/spinup/utils/run_entrypoint.py', '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']' returned non-zero exit status 1.

Does SpinningUp support this enviroments (Robotics) or it is a problem from my side?

opened by RamiSketcher 7

failed to check the installation with mujoco envs
I'm using Ubuntu 14.04 + Python 3.6.7 (Anaconda3-5.3.0-Linux-x86_64).

I have successfully installed mujoco, mujoco_py, gym and spinningup.

mujoco 150 mujoco-py 1.50.1.68 gym 0.10.9 spinup 0.1

And the following code could work:

env = gym.make('Humanoid-v2') env.reset() env.render()

But when I checked the spinningup installation by:

python -m spinup.run ppo --hid [32,32] --env Walker2d-v2 --exp_name installtest

I got the following errors:

Import error. Trying to rebuild mujoco_py. running build_ext building 'mujoco_py.cymj' extension gcc -pthread -B /home/pxlong/anaconda3/envs/spinningup/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py -I/home/pxlong/.mujoco/mjpro150/include -I/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/numpy/core/include -I/home/pxlong/anaconda3/envs/spinningup/include/python3.6m -c /home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py/cymj.c -o /home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py/generated/_pyxbld_1.50.1.68_36_linuxcpuextensionbuilder/temp.linux-x86_64-3.6/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py/cymj.o -fopenmp -w gcc -pthread -B /home/pxlong/anaconda3/envs/spinningup/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py -I/home/pxlong/.mujoco/mjpro150/include -I/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/numpy/core/include -I/home/pxlong/anaconda3/envs/spinningup/include/python3.6m -c /home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py/gl/osmesashim.c -o /home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py/generated/_pyxbld_1.50.1.68_36_linuxcpuextensionbuilder/temp.linux-x86_64-3.6/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py/gl/osmesashim.o -fopenmp -w gcc -pthread -shared -B /home/pxlong/anaconda3/envs/spinningup/compiler_compat -L/home/pxlong/anaconda3/envs/spinningup/lib -Wl,-rpath=/home/pxlong/anaconda3/envs/spinningup/lib -Wl,--no-as-needed -Wl,--sysroot=/ /home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py/generated/_pyxbld_1.50.1.68_36_linuxcpuextensionbuilder/temp.linux-x86_64-3.6/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py/cymj.o /home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py/generated/_pyxbld_1.50.1.68_36_linuxcpuextensionbuilder/temp.linux-x86_64-3.6/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py/gl/osmesashim.o -L/home/pxlong/.mujoco/mjpro150/bin -Wl,-R/home/pxlong/.mujoco/mjpro150/bin -lmujoco150 -lglewosmesa -lOSMesa -lGL -o /home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py/generated/_pyxbld_1.50.1.68_36_linuxcpuextensionbuilder/lib.linux-x86_64-3.6/mujoco_py/cymj.cpython-36m-x86_64-linux-gnu.so -fopenmp Traceback (most recent call last): File "/home/pxlong/Dropbox/git/gym/gym/envs/mujoco/mujoco_env.py", line 11, in import mujoco_py File "/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py/init.py", line 3, in from mujoco_py.builder import cymj, ignore_mujoco_warnings, functions, MujocoException File "/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py/builder.py", line 503, in cymj = load_cython_ext(mjpro_path) File "/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py/builder.py", line 106, in load_cython_ext mod = load_dynamic_ext('cymj', cext_so_path) File "/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/mujoco_py-1.50.1.68-py3.6.egg/mujoco_py/builder.py", line 124, in load_dynamic_ext return loader.load_module() ImportError: dlopen: cannot load any more object with static TLS

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/home/pxlong/Dropbox/git/spinningup/spinup/utils/run_entrypoint.py", line 11, in thunk() File "/home/pxlong/Dropbox/git/spinningup/spinup/utils/run_utils.py", line 162, in thunk_plus thunk(**kwargs) File "/home/pxlong/Dropbox/git/spinningup/spinup/algos/ppo/ppo.py", line 175, in ppo env = env_fn() File "/home/pxlong/Dropbox/git/spinningup/spinup/utils/run_utils.py", line 155, in kwargs['env_fn'] = lambda : gym.make(env_name) File "/home/pxlong/Dropbox/git/gym/gym/envs/registration.py", line 167, in make return registry.make(id) File "/home/pxlong/Dropbox/git/gym/gym/envs/registration.py", line 119, in make env = spec.make() File "/home/pxlong/Dropbox/git/gym/gym/envs/registration.py", line 85, in make cls = load(self._entry_point) File "/home/pxlong/Dropbox/git/gym/gym/envs/registration.py", line 14, in load result = entry_point.load(False) File "/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/pkg_resources/init.py", line 2343, in load return self.resolve() File "/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/site-packages/pkg_resources/init.py", line 2349, in resolve module = import(self.module_name, fromlist=['name'], level=0) File "/home/pxlong/Dropbox/git/gym/gym/envs/mujoco/init.py", line 1, in from gym.envs.mujoco.mujoco_env import MujocoEnv File "/home/pxlong/Dropbox/git/gym/gym/envs/mujoco/mujoco_env.py", line 13, in raise error.DependencyNotInstalled("{}. (HINT: you need to install mujoco_py, and also perform the setup instructions here: https://github.com/openai/mujoco-py/.)".format(e)) gym.error.DependencyNotInstalled: dlopen: cannot load any more object with static TLS. (HINT: you need to install mujoco_py, and also perform the setup instructions here: https://github.com/openai/mujoco-py/.)

================================================================================ There appears to have been an error in your experiment. Check the traceback above to see what actually went wrong. The traceback below, included for completeness (but probably not useful for diagnosing the error), shows the stack leading up to the experiment launch. ================================================================================

Traceback (most recent call last): File "/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/runpy.py", line 193, in _run_module_as_main "main", mod_spec) File "/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/runpy.py", line 85, in _run_code exec(code, run_globals) File "/home/pxlong/Dropbox/git/spinningup/spinup/run.py", line 230, in parse_and_execute_grid_search(cmd, args) File "/home/pxlong/Dropbox/git/spinningup/spinup/run.py", line 162, in parse_and_execute_grid_search eg.run(algo, **run_kwargs) File "/home/pxlong/Dropbox/git/spinningup/spinup/utils/run_utils.py", line 546, in run data_dir=data_dir, datestamp=datestamp, **var) File "/home/pxlong/Dropbox/git/spinningup/spinup/utils/run_utils.py", line 171, in call_experiment subprocess.check_call(cmd, env=os.environ) File "/home/pxlong/anaconda3/envs/spinningup/lib/python3.6/subprocess.py", line 291, in check_call raise CalledProcessError(retcode, cmd) subprocess.CalledProcessError: Command '['python', '/home/pxlong/Dropbox/git/spinningup/spinup/utils/run_entrypoint.py', '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']' returned non-zero exit status 1.
opened by pxlong 7

Fails to run SAC from terminal.

Hi Everyone! I work in a PyCharm environment over Ubuntu 18.04 when I try to execute a variation on this command:

python -m spinup.run sac --hid "[32,32]" --env LunarLander-v2 --exp_name installtest --gamma 0.999

variation means I've tried different combinations of --env, with/without --gamma with/without --hid and its always the next Error Callstack

Here is the Error Callstack:

Saving config:

{
    "ac_kwargs":        {
        "hidden_sizes": [
            32,
            32
        ]
    },
    "actor_critic":     "mlp_actor_critic",
    "alpha":    0.2,
    "batch_size":       100,
    "env_fn":   "<function call_experiment.<locals>.thunk_plus.<locals>.<lambda> at 0x7fc51adfe7b8>",
    "epochs":   100,
    "exp_name": "installtest",
    "gamma":    0.999,
    "logger":   {
        "<spinup.utils.logx.EpochLogger object at 0x7fc51ae0d8d0>":     {
            "epoch_dict":       {},
            "exp_name": "installtest",
            "first_row":        true,
            "log_current_row":  {},
            "log_headers":      [],
            "output_dir":       "/home/asheryartsev/Dev/spinningup/data/installtest/installtest_s0",
            "output_file":      {
                "<_io.TextIOWrapper name='/home/asheryartsev/Dev/spinningup/data/installtest/installtest_s0/progress.txt' mode='w' encoding='UTF-8'>":  {
                    "mode":     "w"
                }
            }
        }
    },
    "logger_kwargs":    {
        "exp_name":     "installtest",
        "output_dir":   "/home/asheryartsev/Dev/spinningup/data/installtest/installtest_s0"
    },
    "lr":       0.001,
    "max_ep_len":       1000,
    "polyak":   0.995,
    "replay_size":      1000000,
    "save_freq":        1,
    "seed":     0,
    "start_steps":      10000,
    "steps_per_epoch":  5000
}
WARNING:tensorflow:From /home/asheryartsev/Dev/spinningup/spinup/algos/sac/sac.py:135: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.

Traceback (most recent call last):
  File "/home/asheryartsev/Dev/spinningup/spinup/utils/run_entrypoint.py", line 11, in <module>
    thunk()
  File "/home/asheryartsev/Dev/spinningup/spinup/utils/run_utils.py", line 162, in thunk_plus
    thunk(**kwargs)
  File "/home/asheryartsev/Dev/spinningup/spinup/algos/sac/sac.py", line 140, in sac
    act_dim = env.action_space.shape[0]
IndexError: tuple index out of range



================================================================================


There appears to have been an error in your experiment.

Check the traceback above to see what actually went wrong. The 
traceback below, included for completeness (but probably not useful
for diagnosing the error), shows the stack leading up to the 
experiment launch.

================================================================================



Traceback (most recent call last):
  File "/home/asheryartsev/anaconda3/envs/spinup/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/asheryartsev/anaconda3/envs/spinup/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/asheryartsev/Dev/spinningup/spinup/run.py", line 230, in <module>
    parse_and_execute_grid_search(cmd, args)
  File "/home/asheryartsev/Dev/spinningup/spinup/run.py", line 162, in parse_and_execute_grid_search
    eg.run(algo, **run_kwargs)
  File "/home/asheryartsev/Dev/spinningup/spinup/utils/run_utils.py", line 546, in run
    data_dir=data_dir, datestamp=datestamp, **var)
  File "/home/asheryartsev/Dev/spinningup/spinup/utils/run_utils.py", line 171, in call_experiment
    subprocess.check_call(cmd, env=os.environ)
  File "/home/asheryartsev/anaconda3/envs/spinup/lib/python3.6/subprocess.py", line 311, in check_call
    raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['/home/asheryartsev/anaconda3/envs/spinup/bin/python', '/home/asheryartsev/Dev/spinningup/spinup/utils/run_entrypoint.py', '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']' returned non-zero exit status 1.

opened by AsherYartsevTech 6

Are there some problems in the ppo code?

Line 259 in ppo.py, buf.store(o,a,r,v_t,logp_t) At the first step in every epoch, we set r=0. So the operation in line 259 results that the first record of reward in the rew_buf is always 0. In fact, we should record the reward after executing pi in the first state.
The operation above causes the problems in the function finish_path. In line62 and line 66, the first record in the rew_buf may result some problems and leave out the last reward during the end of every epoch. To solve this problems, I change the code slightly as below: line262: o_, r, d, =env.step(a[0]) line263: buf.store(o, a, r, v_t, logp_t) line264: ep_ret+ = r line265: ep_len+ = 1 line266: o = o After changing these codes, I also change: line 58: rews = self.rew_buf[path_slice] line 62: deltas=rews + self.gamma * vals[1:] - vals[:-1] line 66:self.ret_buf[path_slice] = core.discount_cumsum(rews, self.gamma)

I don't know whether I am wrong. I am sincerely hoping that you can answer my question. Thank you!

opened by chaobiubiu 6

multi-cpu problem in experiment grid

@jachiam Hi! It's me again! 2 days ago I posted an issue on using multiple cpu on ExperimentGrid that seems to only give the wrong log when run in Pycharm but fine in terminal. I did some more experiments today, and found that although when run from terminal, the log seems to be correct, but there could still be a bug, the proof is that a multi-cpu run will always use more time compared to single cpu run, when solving the same number of jobs.

Here is the code I tried, when you have time, could you try run it and see if you can reproduce the bug? My machine has 4 cores, and using 2 cores to run 4 jobs should be faster than using 1 core to run 4 jobs. The result I get is: single-cpu about 15 seconds, multi-cpu about 20 seconds. I have run this code from terminal, the result indicates that with the same number of jobs, multi-cpu uses more time than single-cpu, which seems a bug to me. I believe what the code might be doing is that for every variant, the same variant has been run num_cpu times.

from spinup.utils.run_utils import ExperimentGrid
from spinup import ppo

import time
if __name__ == '__main__':
    total_num_runs = 4
    import argparse

    ## try multi-cpu case
    parser = argparse.ArgumentParser()
    parser.add_argument('--cpu', type=int, default=2)
    parser.add_argument('--num_runs', type=int, default=total_num_runs)
    args = parser.parse_args()

    ## reset timing
    starttime = time.time()
    eg = ExperimentGrid(name='ppo-bench')
    eg.add('env_name', 'CartPole-v0', '', True)
    eg.add('seed', [10 * i for i in range(args.num_runs)])
    eg.add('epochs', 1)
    eg.add('steps_per_epoch', 200)
    eg.run(ppo, num_cpu=args.cpu)
    multi_cpu_time = time.time() - starttime

    ## try single-cpu case
    args.cpu = 1

    ## reset timing
    starttime = time.time()
    eg = ExperimentGrid(name='ppo-bench')
    eg.add('env_name', 'CartPole-v0', '', True)
    eg.add('seed', [10 * i for i in range(args.num_runs)])
    eg.add('epochs', 1)
    eg.add('steps_per_epoch', 200)
    eg.run(ppo, num_cpu=args.cpu)
    single_cpu_time = time.time() - starttime

    print('single-cpu',single_cpu_time,'multi-cpu',multi_cpu_time)

Many thanks!

opened by watchernyu 6

Do I need tensorflow 1.8?

I am trying to install as specifies in the docs: git clone https://github.com/openai/spinningup.git cd spinningup pip install -e . and I am getting this error: ERROR: Could not find a version that satisfies the requirement tensorflow<2.0,>=1.8.0 (from spinup) (from versions: 2.2.0, 2.2.1, 2.2.2, 2.2.3, 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0rc0, 2.6.0rc1, 2.6.0rc2, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0rc0, 2.7.0rc1, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0rc0, 2.8.0rc1, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0rc0, 2.9.0rc1, 2.9.0rc2, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0rc0, 2.10.0rc1, 2.10.0rc2, 2.10.0rc3, 2.10.0, 2.10.1, 2.11.0rc0, 2.11.0rc1, 2.11.0rc2, 2.11.0) ERROR: No matching distribution found for tensorflow<2.0,>=1.8.0

I tried installing tensorflow but the only installation I could use is 2.2... Does that mean that I need to find a 1.8 tensorflow installation?

Thank you

opened by LinirZamir 0
Running spinningup in WSL2 with WSLg on Windows (Success)
Don't know if this has been detailed before, but I have gotten spinningup to work on WSL2 on windows without needing to install a X server for X11 forwarding. If you didn't know, WSL2 has WSLg which allows for you to run GUI apps in WSL.

First make sure you have WSL2 enabled in your system, you can check by using: wsl -l -v in cmd/powershell. To upgrade to WSL2, issue the command wsl --set-version <distro name> 2 replacing <distro name> with the name of the distribution you want to upgrade.

Next, install the vGPU driver for Ubuntu by following this link here. At the time of this post, the cuda version was 11.8, this will be important later on.

Install miniconda by downloading the .sh from here. Personally, I use pyenv which you can check out here. If you run into any dependency issues creating your virtualenv you might need to find one with python=3.6 support.

Follow spinningup for the rest of the installation.

Here is where we need to potentially fix things. If you encounter no errors, then maybe the issues were patched out. However, when trying to watch the trained policy you might encounter this error:

ImportError: Error occurred while running `from pyglet.gl import *` HINT: make sure you have OpenGL install. On Ubuntu, you can run 'apt-get install python-opengl'. If you're running on a server, you may need a virtual frame buffer; something like this should work: 'xvfb-run -s "-screen 0 1400x900x24" python <your_script.py>'

To fix this issue, install an OpenGL library. The python-opengl library may not be present in your Ubuntu WSL distro, but you can install sudo apt install freeglut3-dev

You might also encounter another error:

libGL error: MESA-LOADER: failed to open swrast: /usr/lib/dri/swrast_dri.so: cannot open shared object file: No such file or directory (search paths /usr/lib/x86_64-linux-gnu/dri:\$${ORIGIN}/dri:/usr/lib/dri, suffix _dri) libGL error: failed to load driver: swrast

Most likely this is due to the anaconda/miniconda install having too old of an GLIBCXX binary (even if your system has a new one). We can check this by exporting this variable: export LIBGL_DEBUG=verbose and running the command again. If we see:

libGL: MESA-LOADER: failed to open /usr/lib/x86_64-linux-gnu/dri/swrast_dri.so: /home/dev/.pyenv/versions/miniconda3-latest/envs/su/bin/../lib/libstdc++.so.6: version `GLIBCXX_3.4.30' not found (required by /lib/x86_64-linux-gnu/libLLVM-13.so.1)

Then we can fix this by symlinking our systems GLIBCXX to conda distribution by executing:

ln -s -f /usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30 $CONDA_PREFIX/lib/libstdc++.so.6

If everything works you should see the training video when running the command again.
opened by datvidwang 0
Documentation: Question about expected return

Hi,

going through the RL Intro I stumbled something that is not yet clear to me. On https://spinningup.openai.com/en/latest/spinningup/rl_intro.html#the-rl-problem for the expected return it says .. math:: J(\pi) = \int_{\tau} P(\tau|\pi) R(\tau) = \underE{\tau\sim \pi}{R(\tau)} where I would have expected .. math:: J(\pi) = \int_{\tau} P(\tau|\pi) R(\tau) = \underE{\tau\sim P}{R(\tau)}

My understanding is that \tau is a RV distributed with respect to P, and only the actions are taken from \pi, as later clearly differentiated on https://spinningup.openai.com/en/latest/spinningup/rl_intro.html#bellman-equations

Please, can someone explain me why it says \tau\sim \pi?

Thank you very much in advance!

opened by aflgit 1
rllib project got moved to another directory; references needs to be changed

rllib moved from this directory to this directory, but almost all of the references in the documentation refer to the old link. (for instance: in the section Why We Built This

Please update the same.

opened by XRFXLP 0
Fixes wrong reference extra PG proof 1

Actually, the changed link references https://spinningup.openai.com/en/latest/spinningup/rl_intro3.html#id16 and not https://spinningup.openai.com/en/latest/spinningup/extra_pg_proof1.html

opened by datajanko 0

Releases(0.2)

0.2(Jan 30, 2020)

Major changes:

Spinning Up now has PyTorch implementations of VPG, PPO, DDPG, TD3, and SAC, in addition to the old Tensorflow versions.

Examples and exercises have been updated to include PyTorch versions as well.

The reward shift bug in the Tensorflow versions of VPG, TRPO, and PPO has been fixed.

DDPG, TD3, and SAC Tensorflow versions were modified so that they now update every N steps instead of at the end of each trajectory. The PyTorch versions of these algorithms have the same behavior.

Spinning Up's SAC has been updated to reflect the more-modern version of SAC that does not use a V-function. The tutorial page on SAC has been updated to describe the new version of SAC.

The benchmark page has been updated with reruns for all algorithms on all environments, using the latest version of the code.
Source code(tar.gz)
Source code(zip)

An educational resource to help anyone learn deep reinforcement learning.

Related tags

Overview

Welcome to Spinning Up in Deep RL!

Citing Spinning Up

Comments

Releases(0.2)

0.2(Jan 30, 2020)

Owner

OpenAI

Language Models for the legal domain in Spanish done @ BSC-TEMU within the "Plan de las Tecnologías del Lenguaje" (Plan-TL).

Neural Point-Based Graphics

Image Segmentation and Object Detection in Pytorch

A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulation

OpenMMLab Detection Toolbox and Benchmark

A unified 3D Transformer Pipeline for visual synthesis

For IBM Quantum Challenge 2021 (May 20 - 26)

Notepy is a full-featured Notepad Python app

SEJE Pytorch implementation

Official code for CVPR2022 paper: Depth-Aware Generative Adversarial Network for Talking Head Video Generation

constructing maps of intellectual influence from publication data

Easy-to-use library to boost AI inference leveraging state-of-the-art optimization techniques.

Automatic detection and classification of Covid severity degree in LUS (lung ultrasound) scans

Code for testing various M1 Chip benchmarks with TensorFlow.

Localization Distillation for Object Detection

Lightweight Python library for adding real-time object tracking to any detector.

Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration

The codes and related files to reproduce the results for Image Similarity Challenge Track 1.

MVSDF - Learning Signed Distance Field for Multi-view Surface Reconstruction

PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).